library(tidyverse)
library(plotly)
mpg %>%
mutate(cyl = as_factor(cyl)) %>%
ggplot(aes(displ, hwy, color = cyl)) + geom_point()
mpg %>%
plot_ly(x = ~displ, y = ~hwy, color = ~ factor(cyl))
Pros: - Interactive - Plotly handles multiple wide data columns (ggplot2 requies long format) - Plotly works for Python, Matlab, and Excel, among other languages - Easy layout customization - 3D charts
Cons: - Doesn’t work very well with pdf - Facet wrapping is a bit complicated compared with ggplot2 - adding legend title is difficult
ggplot(mpg, aes(displ, hwy)) +
geom_point(data = mutate(mpg, cyl = NULL), color = "grey75") +
geom_point() +
facet_wrap(vars(cyl))
ggplotly function for ggplot2 usersp <- mpg %>%
mutate(cyl = as_factor(cyl)) %>%
ggplot(aes(displ, hwy, color = cyl)) + geom_point()
ggplotly(p)
ggplot2 (or should we?)
HTML, SVG, CSS, JavaScript
d3.js (R package r2d3)
p <- economics %>%
sample_n(n()) %>%
plot_ly(x = ~date, y = ~psavert)
p %>% add_paths() # using the order of the data frame
p %>% add_lines()
library(lubridate)
##
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
##
## date
econ <- economics %>%
mutate(yr = year(date), mnth = month(date))
# One trace (more performant, but less interactive)
econ %>%
group_by(yr) %>%
plot_ly(x = ~mnth, y = ~uempmed) %>%
add_lines(text = ~yr)
# Multiple traces (less performant, but more interactive)
econ %>%
mutate(yr = ordered(yr)) %>%
plot_ly(x = ~mnth, y = ~uempmed) %>%
add_lines(color = ~yr)
plot_ly(econ, x = ~mnth, y = ~uempmed) %>%
add_lines(color = ~ ordered(yr)) %>%
toWebGL()
mpg %>%
plot_ly(x = ~cty, y = ~hwy) %>%
add_markers(alpha = 0.2)
mtcars %>%
plot_ly(x = ~disp, y = ~mpg) %>%
add_markers(color = ~ factor(cyl))
mtcars %>%
plot_ly(x = ~disp, y = ~mpg) %>%
add_markers(symbol = ~ factor(cyl))
mpg %>%
group_by(cyl) %>%
summarize(mhwy = mean(hwy), se = sd(hwy) / sqrt(n())) %>%
plot_ly(x = ~mhwy, y = ~ factor(cyl)) %>%
add_markers(error_x = ~ list(value = se)) %>%
layout(xaxis = list(title = "mean hwy"), yaxis = list(title = "cyl"))
mpg %>%
group_by(model) %>%
summarize(c = mean(cty), h = mean(hwy)) %>%
mutate(model = forcats::fct_reorder(model, c)) %>%
plot_ly() %>%
add_segments(
x = ~c, y = ~model,
xend = ~h, yend = ~model,
color = I("gray"), showlegend = FALSE
) %>%
add_markers(
x = ~c, y = ~model,
color = I("blue"),
name = "mpg city"
) %>%
add_markers(
x = ~h, y = ~model,
color = I("red"),
name = "mpg highway"
) %>%
layout(xaxis = list(title = "Miles per gallon"))
mpg %>%
plot_ly(x = ~hwy, color = ~ factor(cyl)) %>%
add_histogram(histnorm = "", alpha = 0.7) %>% # histnorm could be "", "probability", "density" and "probability density"
layout(barmode = "overlay") # barmode could be "overlay", "stack" and "group"
names(relig_income) %>%
paste0(collapse = "\n") %>%
cat()
## religion
## <$10k
## $10-20k
## $20-30k
## $30-40k
## $40-50k
## $50-75k
## $75-100k
## $100-150k
## >150k
## Don't know/refused
mpg %>%
mutate(cyl = as_factor(cyl)) %>%
count(drv, cyl) %>%
plot_ly(x = ~drv, y = ~n, color = ~cyl) %>%
add_bars() %>%
layout(barmode = "stack")
# work with wide format directly
relig_income %>%
mutate(religion = as_factor(religion)) %>%
plot_ly(y = ~religion) %>%
add_bars(~`<$10k`, name = "<$10k") %>%
add_bars(~`$10-20k`, name = "$10-20k") %>%
add_bars(~`$20-30k`, name = "$20-30k") %>%
add_bars(~`$30-40k`, name = "$30-40k") %>%
add_bars(~`$40-50k`, name = "$40-50k") %>%
add_bars(~`$50-75k`, name = "$50-75k") %>%
add_bars(~`$75-100k`, name = "$75-100k") %>%
add_bars(~`$100-150k`, name = "$100-150k") %>%
add_bars(~`>150k`, name = ">150k") %>%
add_bars(~`Don't know/refused`, name = "Don't know/refused") %>%
layout(xaxis = list(title = "count"), barmode = "stack")
# may be easier with `pivot_longer`?
relig_income %>%
mutate(religion = as_factor(religion)) %>%
pivot_longer(-religion, names_to = "income", values_to = "count") %>%
mutate(income = fct_inorder(income)) %>%
plot_ly(x = ~count, y = ~religion, color = ~income) %>%
add_bars() %>%
layout(barmode = "stack")
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
mpg %>%
mutate(cyl = as_factor(cyl)) %>%
plot_ly(x = ~drv, y = ~hwy, color = ~cyl) %>%
add_boxplot() %>%
layout(boxmode = "group")
## Warning: 'layout' objects don't have these attributes: 'boxmode'
## Valid attributes include:
## 'font', 'title', 'autosize', 'width', 'height', 'margin', 'paper_bgcolor', 'plot_bgcolor', 'separators', 'hidesources', 'showlegend', 'colorway', 'datarevision', 'uirevision', 'editrevision', 'selectionrevision', 'template', 'modebar', 'meta', 'transition', '_deprecated', 'clickmode', 'dragmode', 'hovermode', 'hoverdistance', 'spikedistance', 'hoverlabel', 'selectdirection', 'grid', 'calendar', 'xaxis', 'yaxis', 'ternary', 'scene', 'geo', 'mapbox', 'polar', 'radialaxis', 'angularaxis', 'direction', 'orientation', 'editType', 'legend', 'annotations', 'shapes', 'images', 'updatemenus', 'sliders', 'colorscale', 'coloraxis', 'metasrc', 'barmode', 'bargap', 'mapType'
library(gapminder)
gapminder2007 <- gapminder %>%
filter(year == 2007) %>%
mutate(pop = as.double(pop))
bind_rows(
gapminder2007 %>%
select(children = country, parents = continent, pop = pop),
gapminder2007 %>%
group_by(continent) %>%
summarize(pop = sum(pop)) %>%
transmute(children = continent, parents = "World", pop = pop),
gapminder2007 %>%
summarize(pop = sum(pop)) %>%
transmute(children = "World", parents = "", pop = pop)
) %>%
plot_ly(
ids = ~children,
labels = ~children,
parents = ~parents,
values = ~pop,
type = "sunburst",
branchvalues = "total")
mpg %>%
mutate(cyl = as_factor(cyl)) %>%
plot_ly(x = ~cty, y = ~hwy, z = ~cyl) %>%
add_markers(color = ~cyl)
x <- seq_len(nrow(volcano)) + 100
y <- seq_len(ncol(volcano)) + 500
plot_ly(x = ~x, y = ~y, z = ~volcano) %>%
add_surface(colorscale = "Earth")
# heatmap
plot_ly(x = ~x, y = ~y, z = ~volcano) %>%
add_heatmap(colorscale = "Earth")
plot_ly(x = ~x, y = ~y, z = ~volcano) %>%
add_contour(colorscale = "Earth")
df <- tibble(
x = c(1, 2, 1),
y = c(1, 2, 1),
t = c(1, 2, 3)
)
df %>%
plot_ly(x = ~x, y = ~y, frame = ~t, showlegend = F)
## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plot.ly/r/reference/#scatter
## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
library(gapminder)
(p <- gapminder %>%
plot_ly(x = ~lifeExp, y = ~gdpPercap, size = ~pop, color = ~continent, frame = ~year) %>%
layout(yaxis = list(type = "log")))
r2d3r2d3 is an R binding to the famous javascript library d3.js. Visit https://github.com/d3/d3/wiki/Gallery to see some of the things that d3.js is able to create.